As the United States is on the cusp of returning astronauts to the Moon, it becomes increasingly apparent that the assembly of structures in space will have to rely upon robots to perform the construction process. With a focus on sustaining a presence on the Moon's surface in such a harsh and unforgiving environment, demonstrating the robustness of autonomous assembly and capabilities of robotic manipulators is necessary. Current robotic assembly on Earth consists mainly of inspection or highly controlled environments, and always with a human in the loop to step in and fix issues if a problem occurs. To remove the human element, the robot system then must account for safety as well. Thus, system risk can easily overwhelm project costs. This thesis proposes a combination of hardware and state estimation solutions to improve the feasibility of low-fidelity and low-resolution robots for precision assembly tasks. Doing so reduces the risk to mission success, as the hardware becomes easier to replace or repair. The hardware modifications implement an electro-permanent magnet interface with alignment features to reduce the fidelity needed for the robot end effector. On the state estimation side, an Ensemble Kalman Filter is implemented, along with a scaling system to prevent FASER Lab hardware from becoming stuck due to hardware limitations. Overall, the three modifications improved the test robot's autonomous convergence error by 98.5%, bettering the system sufficiently to make an autonomous assembly process feasible. / Master of Science / With the dawn of new space age nearly upon us, one of the most important aspects to working in space will be robotic assembly, whether on the surface of other planetary bodies like the Moon or in zero-gravity, in order to keep astronauts safe and to reduce spaceship launch costs. Both places have their own difficult problems to deal with, and doing any actions in those locations come with a significant amount of risk involved. To reduce extreme risk, you can spend more money to over-protect the robots, or reduce the consequences of the risk. This thesis describes a way to reduce the impact of risks to a mission by checking whether inexpensive robots can be adapted and modified to be able to perform similar construction actions to a much more expensive robot. It does this by using specialized hardware and software programs to better align the robot to where it needs to go without people needing to step in and help it. The experiments showed a 98.5% improvement to the system from without any of the modifications and validated that the low-cost robot could be improved sufficiently to be useful.

This thesis details the implementation of two adaptive controllers on autonomous underwater vehicle(AUV) attitude dynamics starting from the standard six degree-of-freedom dynamic model. I apply two model reference adaptive control (MRAC) algorithms which make use of kernel functions for learning functional uncertainty present in the system dynamics. The first method extends recent results on model reference adaptive control using reproducing kernel Hilbert space (RKHS) learning techniques for some general cases of multi-input systems. The first controller design is a model reference adaptive controller (MRAC) based on a vector- valued RKHS that is induced by operator-valued kernels. This paper formulates a model reference adaptive control strategy based on a dead zone robust modification, and derives conditions for the ultimate boundedness of the tracking error in this case. The second controller is an implementation of the Gaussian Process MRAC developed by Chowdhary, et al. I discuss the method of each of these algorithms before contrasting the underlying theoretical structure of each algorithm. Finally, I provide a comparison of each algorithm's performance on the six degree-of-freedom dynamic model of the Virginia Tech 690 AUV and provide field trial results for the RKHS based MRAC implementation. / Master of Science / This thesis details the implementation of two algorithms which control the attitude of an autonomous underwater vehicle. Rather than developing detailed dynamic models of the vehicles as is performed in classical control methods, each of these implementations only makes assumptions that the unknown portions of the dynamic models can be represented by a broad class of functions defined by a mathematical structure called a reproducing kernel Hilbert Space. Each algorithm implements learning techniques using the theory of reproducing kernel Hilbert spaces to bound the error between the vehicle attitude and the commanded vehicle attitude. One algorithm, called RKHS MRAC, implements an adaptive update law based on the attitude error to improve the controller performance. The second algorithm, called GP MRAC, uses estimated vehicle rotational accelerations and statistical learning methods to approximate the unknown function. Each of these methods is compared in theory and in a vehicle simulation. The RKHS MRAC is additionally demonstrated in field trial results.

Navigation of autonomous underwater vehicles (AUVs) in the subsea environment is particularly challenging due to the unavailability of GPS because of rapid attenuation of electromagnetic waves in water. As a result, the AUV requires alternative methods for position estimation. This thesis describes a terrain-aided navigation approach for an AUV where, with the help of a prior depth map, the AUV localizes itself using altitude measurements from a multibeam DVL. The AUV simultaneously builds a probabilistic depth map of the seafloor as it moves to unmapped locations. The main contribution of this thesis is a new, scalable, and on-line terrain-aided navigation solution for AUVs which does not require the assistance of a support surface vessel. Simulation results on synthetic data and experimental results from AUV field trials in Panama City, Florida are also presented. / Master of Science / Navigation of autonomous underwater vehicles (AUVs) in subsea environment is particularly challenging due to the unavailability of GPS because of rapid attenuation of electromagnetic waves in water. As a result, the AUV requires alternative methods for position estimation. This thesis describes a terrain-aided navigation approach for an AUV where, with the help of a prior depth map, the AUV localizes itself using altitude measurements from a multibeam DVL. The AUV simultaneously builds a probabilistic depth map of the seafloor as it moves to unmapped locations. The main contribution of this thesis is a new, scalable, and on-line terrain-aided navigation solution for AUVs which does not require assistance of a support surface vessel. Simulation results on synthetic data and experimental results from AUV field trials in Panama City, Florida are also presented.

Unmanned vehicles are often used in time-critical missions such as reconnaissance or search and rescue. To this end, this thesis provides autonomous localization and mapping tools for 1/R² sources. A "1/R²" source is one in which the received intensity of the source is inversely proportional to the square of the distance from the source. An autonomous localization algorithm is developed which utilizes a particle swarm particle ltering method to recursively estimate the location of a source. To implement the localization algorithm experimentally, a command interface with Virginia Tech's autonomous helicopter was developed. The interface accepts state information from the helicopter, and returns command inputs to drive the helicopter autonomously to the source. To make the use of the system more intuitive, a graphical user interface was developed which provides localization functionality as well as a waypoint navigation outer-loop controller for the helicopter. This assists in positioning the helicopter and returning it home after the the algorithm is completed. An autonomous mapping mission with a radioactive source is presented, along with a localization experiment utilizing simulated sensor readings. This work is the rst phase of an on-going project at the Unmanned Systems Lab. Accordingly, this thesis also provides a framework for its continuation in the next phase of the project. / Master of Science

The Virginia Tech self-mooring autonomous underwater vehicle (AUV) is capable of mooring itself on the seafloor for extended periods of time. The AUV is intended to travel to a desired mooring location, moor itself on the seafloor, and then release the mooring and return to a desired egress location. The AUV is designed to be an inexpensive sensor platform. The AUV utilizes a false nose that doubles as an anchor. The anchor is neutrally buoyant when attached to the AUV nose. When the vehicle moors it releases the false nose, which floods the anchor making it heavy, sinking both the anchor and AUV to the seafloor. At the end of the mooring time the vehicle releases the anchor line and travels to the recovery location. A prototype vehicle was constructed from a small-scale platform known as the Virginia Tech 475 AUV and used to test the self-mooring concept. The final self-mooring AUV was then constructed to perform the entire long duration mission. The final vehicle was tested successfully for an abbreviated mission profile. This report covers the general design elements of the self-mooring AUV, the detailed design of both the prototype and final AUVs, and the results of successful field trials with both vehicles. / Master of Science

This study addresses the limitations of existing autonomous vehicle datasets, particularly the need for greater specificity of weather conditions and road types. We utilized X-CAR to highlight the challenges of extreme weather and non-urban road conditions on autonomous driving systems. Our dataset comprises recordings under seven distinct weather and lighting conditions across four road types. Notably, our research focuses on differentiating between various lighting and weather conditions and road types, which often need improvement in many existing datasets. We used the X-CAR platform to collect 360-degree image information and LiDAR point clouds at 10Hz. Due to the constraints of time and resources, we used algorithmic prediction to generate ground truth data via the Co-DETR 2D prediction algorithm. We validated the accuracy of the Co-DETR algorithm through partial manual annotation. However, it is undeniable that in some extreme conditions, the algorithm-generated ground truth can lead to results deviating from expectations and real-world situations. Therefore, we conducted a scaled manual annotation and controlled experiments, ensuring the highest level of accuracy. After the manual annotation, we validated our initial conclusions and trained a model based on YOLOv8x, focusing on weak environmental conditions. The final model underwent multiple iterations and achieved satisfactory accuracy. The enhanced model demonstrated a significant increase in detection accuracy compared to the original YOLOv8x model. At the same time, our analysis identifies weather conditions that markedly reduce detection accuracy, providing focal points for future dataset enhancements. / Master of Science / This study explores the limitations of current autonomous vehicle datasets, particularly their lack of detail regarding weather conditions and road types. We used X-CAR to examine how extreme weather and light conditions affect autonomous driving systems. Our dataset includes recordings from seven different weather and lighting conditions across four types of roads. Due to time and resource constraints, we used an algorithm to predict ground truth data with the help of Co-DETR. While not all data was fully annotated, we manually labeled part of the data to create an actual ground truth. This allowed us to verify our previous findings and train a model based on YOLOv8x, focusing on challenging conditions. The improved model showed much higher accuracy in detecting objects than the original YOLOv8x model. This study highlights the significant impact of weather conditions on detection accuracy and suggests areas for future improvements in datasets.

Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and the POSIT algorithm uses these feature points to estimate the pose (position and attitude) of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm's performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance. / Master of Science / Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and a pose (position and attitude) estimation algorithm uses these feature points to estimate the pose of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm’s performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance.

published_or_final_version / Earth Sciences / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Earth Sciences / Master / Master of Philosophy

published_or_final_version / abstract / Earth Sciences / Master / Master of Philosophy